Identifying cancer using MRI and CT scans

A tétel rövid nézete
dc.contributor.advisorTomán, Henrietta
dc.contributor.authorKumar, Dheeraj
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2023-04-28T09:00:06Z
dc.date.available2023-04-28T09:00:06Z
dc.date.created2023
dc.description.abstractThis thesis presented a thorough investigation into developing a machine learning model for the classification of Renal Carcinoma Cancer in medical images. The primary aim was to create an accurate and efficient classifier capable of differentiating between malignant and non-cancerous images, thus contributing to earlier cancer detection and improved patient outcomes. The research commenced with an extensive literature review and selection of five renal cancer-related datasets. The chosen dataset was preprocessed using techniques such as outlier removal, denoising, edge detection, histogram equalization, and data augmentation. Several machine learning models, including ANN, SVM, CNN's VGG16, and Transformer's Swin Transformer, were assessed. The SVM model achieved the highest level of accuracy and precision, while the Transformer model exhibited the best recall. The user-friendly website provided instant feedback on whether the uploaded image was malignant or noncancerous.
dc.description.correctorN.I.
dc.description.courseComputer Science
dc.description.degreeBSc/BA
dc.format.extent89
dc.identifier.urihttps://hdl.handle.net/2437/351190
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectMachine Learning Models Analysis
dc.subjectKidney Cancer Detection in Medical Imaging
dc.subjectIncreasing Accuracy using Preprocessing
dc.subject.dspaceDEENK Témalista::Informatika
dc.titleIdentifying cancer using MRI and CT scans
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